Community Discovery Library

Implementation of the Leiden algorithm for various quality functions to be used with igraph in Python.

Open-source graph database, tuned for dynamic analytics environments. Easy to adopt, scale and own.

A tutorial on organizing matplotlib plots

Code for the paper "Fractional diffusion theory of balanced heterogeneous neural networks"

Some overlapping community detection algorithms (Until 2016). by Yulin Che (https://github.com/CheYulin) for the PhD qualification exam (survey on community detection algorithms)

Publication-quality network visualisations in python

First set of data and R code for working-memory benchmarks.

'Visualization in Bayesian workflow' by Gabry, Simpson, Vehtari, Betancourt, and Gelman. (JRSS discussion paper and code)

Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.

Important concepts in numerical linear algebra and related areas

"我的阅历"

Weighted Directed Networks used in the publication "Community Detection in Networks using Bio-inspired Computation: Recent History, New Results and Perspectives"

An R package for hierarchical clustering with p-values

Finds the clusters in a directed graph and gives a scalar value (Q-modularity index) that indicates how well clustered are the data

Codes used in my master's thesis project @ PPW KU Leuven

Software for generating a brain-predicted age value, using Gaussian Processes regression, implemented in R

Harmonization of multi-site imaging data with ComBat

Implementation of various multi-armed bandits algorithms on a 10-arm testbed.

Tutorial talks at the Chinese Open Science Webinar series

R package for the simulation of the prior distribution of bayesian trees by Chipman et al. (1998).

Implements Anderson (1991)'s rational model and a few of its successors.

Material for "Drawing Anything with ggplot2" workshop

Formal Psychological Models of Categorization and Learning

This folder contains two hypothesis tests covered in my ICML work for when pooling multi-source datasets can help.

京东ＪＤＤ大数据比赛解决方案(baseline)

2017-CCF-BDCI-企业经营退出风险预测：9th/569 (Top 1.58%)